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arXiv:2610.10405v1 Announce Type: cross Abstract: Monitoring the chain-of-thought of reasoning artificial intelligence (AI) models remains a key approach to detecting deception and other forms of mis...
The study investigates whether the number of reasoning tokens produced by large language models can signal untruthful responses. Researchers prompted three models to answer 210 multiple‑choice questions under instructions to be truthful, deceptive, or indifferent, and found that truth‑directed answers consistently used fewer reasoning tokens than lie‑directed or indifferent ones. The results suggest that token count could serve as a simple, content‑independent indicator of untruthful behavior when full reasoning traces are unavailable.
The paper introduces the concept of semantic deception, where unfamiliar symbols are used to mislead large language models (LLMs) during simple arithmetic tasks. By redefining digits and operators with novel symbols, the authors test whether LLMs can abstract and manipulate arbitrary symbol systems or fall back on learned semantic associations. Experiments with four LLMs show that misleading semantic cues significantly degrade performance, revealing a reliance on surface-level semantics that can undermine symbolic reasoning.
arXiv:2608. 03291v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning process.
arXiv:2607. 29062v1 Announce Type: new Abstract: Model capabilities have improved in large part due to scaling chain of thought.
arXiv:2607. 11266v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of Large Language Models (LLMs), yet it often incurs substantial computational costs due to over-reasoning: the generation of redundant, verbose, or irrelevant steps.